Bayesian Statistics
What you'll learn
- Bayes Theorem
- Conditional & Absolute independence
- Bayesian networks & d separation
- Enumeration & Elimination
- Sampling methods (rejection sampling, Gibbs sampling, Metropolis Hastings)
- Bayesian inference
- Continuous Bayesian statistics
- Bayesian statistics & machine learning
Requirements
- High school level mathematics / ideally first-year university mathematics or statistics course
- Basic background in probability
Description
Bayesian Statistics is a fascinating field and today the centerpiece of many statistical applications in data science and machine learning. In this course, we will cover the main concepts of Bayesian Statistics including among others Bayes Theorem, Bayesian networks, Enumeration & Elimination for inference in such networks, sampling methods such as Gibbs sampling and the Metropolis-Hastings algorithm, Bayesian inference and the relation to machine learning.
This course is designed around examples and exercises that provide plenty of opportunities to build intuition and apply your gathered knowledge. Many examples come from real-world applications in science, business or engineering or are taken from data science job interviews.
While this is not a programming course, I have included multiple references to programming resources relevant to Bayesian statistics. The course is specifically designed for students without many years of formal mathematical education. The only prerequisite is high-school level mathematics, ideally a first-year university mathematics course and a basic understanding of probability.
Who this course is for:
- University students in science, business and engineering interested in learning about Bayesian Statistics for university or job interviews
- Practitioners in these fields interested in learning the central concepts of Bayesian statistics to apply them to real-world problems
Course content
- Preview01:53
- Preview02:34
- Preview03:51
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- 1 questionPrimer on probability - Exercise 1
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- 1 questionPrimer on probability - Exercise 2
- Preview01:59
- Preview03:10
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- Preview00:26
Instructor
![Philipp Loick](data:image/svg+xml,%3Csvg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 64 64"%3E%3C/svg%3E){kind=avatar}
- 4.5 Instructor Rating
- 180 Reviews
- 1,793 Students
- 1 Course
I hold a PhD student in Mathematics with a focus on Bayesian statistics and the theory of machine learning at Goethe University Frankfurt in Germany. My research has been published, among others, in the Proceedings of Machine Learning Research. Previously, I have obtained a Master in operations research from the London School of Economics and a Master in computer science from the Georgia Institute of Technology. I am currently working as an Applied Scientist at Amazon.
When I started learning about Bayesian statistics during my Master program, it took me quite some time to compile the relevant resources and digest the body of majorly theoretical material. Since then, it has been my goal to offer a course that provides an easy-to-understand and intuitive discussion of the concepts in Bayesian Statistics. This is my stab at it - I hope, you enjoy it!